1. Field of the Invention
The present invention relates to the field of computer aided diagnosis of abnormalities in medical images. In particular, the invention relates to a method and system for using local attention in the detection of clustered microcalcifications in digital mammograms to assist in the detection of malignant breast cancer tumors at an early stage.
2. Description of the Related Art:
Breast cancer in women is a serious health problem, the American Cancer Society currently estimating that over 180,000 women in the United States are diagnosed with breast cancer each year. Breast cancer is the second major cause of cancer death among women; the American Cancer Society also estimates that breast cancer causes the death of over 44,000 women in the United States each year. While at present there is no means for preventing breast cancer, early detection of the disease prolongs life expectancy and decreases the likelihood of the need for a total mastectomy. Mammography using x-rays is currently the most common method of detecting and analyzing breast lesions.
The detection of clustered microcalcifications in mammograms is of particular importance because tumors having such microcalcifications have a relatively high probability of being malignant. While it is important to detect clustered microcalcifications as early as possible, practical considerations can make this difficult. In particular, the microcalcifications may be subtle and faint, and a typical radiologist may be required to examine hundreds of mammograms per day, leading to the possibility of a missed diagnosis due to human error.
Accordingly, the need has arisen for a computer-assisted diagnosis/detection (CAD) system for assisting in the detection of abnormal lesions, including clustered microcalcifications, in medical images. The desired CAD system digitizes x-ray mammograms to produce a digital mammogram, and performs numerical image processing algorithms on the digital mammogram. The output of the CAD system is a highlighted display which directs the attention of the radiologist to suspicious portions of the x-ray mammogram.
The desired characteristics of a clustered microcalcification-detecting CAD system are high speed (requiring less processing time), high precision and high accuracy (the ability to detect subtle microcalcifications and avoid false positives).
One system which uses a Shift Invariant Neural Network for the detection of clustered microcalcifications is described in U.S. Pat. No. 5,491,627 to Zhang et al. entitled "METHOD AND SYSTEM FOR THE DETECTION OF MICROCALCIFICATIONS IN DIGITAL MAMMOGRAMS". However, one drawback of such systems is that they are not able to detect or often miss very subtle clusters of microcalcifications.